Overview

Dataset statistics

Number of variables21
Number of observations2260686
Missing cells193160
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory362.2 MiB
Average record size in memory168.0 B

Variable types

CAT11
NUM10

Warnings

order_created_at has a high cardinality: 2044439 distinct values High cardinality
order_completed_at has a high cardinality: 2025311 distinct values High cardinality
shipment_starts_at has a high cardinality: 7221 distinct values High cardinality
s.city_name has a high cardinality: 97 distinct values High cardinality
shipped_at has a high cardinality: 1864234 distinct values High cardinality
shipment_id is highly correlated with ship_address_id and 1 other fieldsHigh correlation
ship_address_id is highly correlated with shipment_id and 1 other fieldsHigh correlation
order_id is highly correlated with ship_address_id and 1 other fieldsHigh correlation
os is highly correlated with platformHigh correlation
platform is highly correlated with osHigh correlation
shipped_at has 185016 (8.2%) missing values Missing
promo_total is highly skewed (γ1 = -21.09994488) Skewed
order_created_at is uniformly distributed Uniform
order_completed_at is uniformly distributed Uniform
shipped_at is uniformly distributed Uniform
shipment_id has unique values Unique
total_cost has 494018 (21.9%) zeros Zeros
rate has 856368 (37.9%) zeros Zeros
promo_total has 1630341 (72.1%) zeros Zeros

Reproduction

Analysis started2020-10-17 15:40:13.290907
Analysis finished2020-10-17 15:46:40.443974
Duration6 minutes and 27.15 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

Distinct872758
Distinct (%)38.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366535.6774
Minimum0
Maximum872757
Zeros4
Zeros (%)< 0.1%
Memory size17.2 MiB
2020-10-17T18:46:40.992509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28258
Q1141292.25
median329598.5
Q3576502
95-th percentile802570.75
Maximum872757
Range872757
Interquartile range (IQR)435209.75

Descriptive statistics

Standard deviation250381.3771
Coefficient of variation (CV)0.6831023351
Kurtosis-1.102031449
Mean366535.6774
Median Absolute Deviation (MAD)207838.5
Skewness0.3425850119
Sum8.286220745e+11
Variance6.269083401e+10
MonotocityNot monotonic
2020-10-17T18:46:41.145102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20474< 0.1%
 
1097284< 0.1%
 
1179324< 0.1%
 
1281714< 0.1%
 
1302184< 0.1%
 
1240734< 0.1%
 
1261204< 0.1%
 
1035914< 0.1%
 
1056384< 0.1%
 
994934< 0.1%
 
Other values (872748)2260646> 99.9%
 
ValueCountFrequency (%) 
04< 0.1%
 
14< 0.1%
 
24< 0.1%
 
34< 0.1%
 
44< 0.1%
 
ValueCountFrequency (%) 
8727571< 0.1%
 
8727561< 0.1%
 
8727551< 0.1%
 
8727541< 0.1%
 
8727531< 0.1%
 

user_id
Real number (ℝ≥0)

Distinct654907
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419608.138
Minimum1400
Maximum2925501
Zeros0
Zeros (%)0.0%
Memory size17.2 MiB
2020-10-17T18:46:41.625818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1400
5-th percentile200558
Q1889145
median1466336
Q31938162
95-th percentile2574073.5
Maximum2925501
Range2924101
Interquartile range (IQR)1049017

Descriptive statistics

Standard deviation716489.8138
Coefficient of variation (CV)0.5047095705
Kurtosis-0.7707003322
Mean1419608.138
Median Absolute Deviation (MAD)517211
Skewness-0.09404034812
Sum3.209288244e+12
Variance5.133576533e+11
MonotocityNot monotonic
2020-10-17T18:46:41.834260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
57430395< 0.1%
 
346285214< 0.1%
 
959397194< 0.1%
 
226064184< 0.1%
 
301738164< 0.1%
 
1421169154< 0.1%
 
621722134< 0.1%
 
353314132< 0.1%
 
145608131< 0.1%
 
1094835129< 0.1%
 
Other values (654897)225885599.9%
 
ValueCountFrequency (%) 
14002< 0.1%
 
145934< 0.1%
 
15401< 0.1%
 
15411< 0.1%
 
15774< 0.1%
 
ValueCountFrequency (%) 
29255011< 0.1%
 
29254871< 0.1%
 
29254861< 0.1%
 
29254841< 0.1%
 
29254801< 0.1%
 

ship_address_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2226096
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7795237.668
Minimum8531
Maximum12540602
Zeros0
Zeros (%)0.0%
Memory size17.2 MiB
2020-10-17T18:46:43.489836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum8531
5-th percentile3093373.75
Q15786986.75
median8025621.5
Q310024670.75
95-th percentile11799473.5
Maximum12540602
Range12532071
Interquartile range (IQR)4237684

Descriptive statistics

Standard deviation2716492.13
Coefficient of variation (CV)0.3484809888
Kurtosis-0.8818737825
Mean7795237.668
Median Absolute Deviation (MAD)2098333.5
Skewness-0.2786823699
Sum1.762258466e+13
Variance7.379329495e+12
MonotocityNot monotonic
2020-10-17T18:46:43.678332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
59984444< 0.1%
 
65897854< 0.1%
 
85112994< 0.1%
 
99507784< 0.1%
 
61703864< 0.1%
 
86874884< 0.1%
 
39655444< 0.1%
 
102825514< 0.1%
 
66463544< 0.1%
 
99386004< 0.1%
 
Other values (2226086)2260646> 99.9%
 
ValueCountFrequency (%) 
85311< 0.1%
 
174101< 0.1%
 
233641< 0.1%
 
295761< 0.1%
 
450291< 0.1%
 
ValueCountFrequency (%) 
125406021< 0.1%
 
125405911< 0.1%
 
125405881< 0.1%
 
125405581< 0.1%
 
125404351< 0.1%
 

shipment_id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct2260686
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6394665.419
Minimum178163
Maximum9916560
Zeros0
Zeros (%)0.0%
Memory size17.2 MiB
2020-10-17T18:46:45.039695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum178163
5-th percentile2425272
Q14831396.25
median6588654
Q38215820.75
95-th percentile9572736.75
Maximum9916560
Range9738397
Interquartile range (IQR)3384424.5

Descriptive statistics

Standard deviation2193967.791
Coefficient of variation (CV)0.3430934455
Kurtosis-0.8942715243
Mean6394665.419
Median Absolute Deviation (MAD)1685556.5
Skewness-0.3156400708
Sum1.445633059e+13
Variance4.813494669e+12
MonotocityNot monotonic
2020-10-17T18:46:45.212235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
89794981< 0.1%
 
94803441< 0.1%
 
59949481< 0.1%
 
59826581< 0.1%
 
58106221< 0.1%
 
58126691< 0.1%
 
58024261< 0.1%
 
58065201< 0.1%
 
58269981< 0.1%
 
85851441< 0.1%
 
Other values (2260676)2260676> 99.9%
 
ValueCountFrequency (%) 
1781631< 0.1%
 
2739881< 0.1%
 
3223071< 0.1%
 
3378091< 0.1%
 
3517621< 0.1%
 
ValueCountFrequency (%) 
99165601< 0.1%
 
99165401< 0.1%
 
99165321< 0.1%
 
99165191< 0.1%
 
99165171< 0.1%
 

order_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2226106
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10800948.79
Minimum3217
Maximum15908203
Zeros0
Zeros (%)0.0%
Memory size17.2 MiB
2020-10-17T18:46:46.579580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3217
5-th percentile5935478.75
Q18612992.5
median10964336
Q313194186
95-th percentile15099057.75
Maximum15908203
Range15904986
Interquartile range (IQR)4581193.5

Descriptive statistics

Standard deviation2872840.54
Coefficient of variation (CV)0.2659803871
Kurtosis-0.9276173959
Mean10800948.79
Median Absolute Deviation (MAD)2281872.5
Skewness-0.2131980498
Sum2.441755371e+13
Variance8.253212767e+12
MonotocityNot monotonic
2020-10-17T18:46:46.741148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
148097334< 0.1%
 
117106824< 0.1%
 
94824134< 0.1%
 
68663424< 0.1%
 
139869244< 0.1%
 
94232594< 0.1%
 
99655564< 0.1%
 
150983754< 0.1%
 
96703034< 0.1%
 
70165824< 0.1%
 
Other values (2226096)2260646> 99.9%
 
ValueCountFrequency (%) 
32171< 0.1%
 
1391281< 0.1%
 
1398651< 0.1%
 
1411891< 0.1%
 
1417361< 0.1%
 
ValueCountFrequency (%) 
159082031< 0.1%
 
159081891< 0.1%
 
159081831< 0.1%
 
159081461< 0.1%
 
159080121< 0.1%
 

order_created_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2044439
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
2020-03-04 14:24:13
 
7
2020-07-11 10:49:56
 
6
2020-08-07 12:57:09
 
6
2020-06-14 11:16:32
 
6
2020-07-09 07:04:24
 
6
Other values (2044434)
2260655 
ValueCountFrequency (%) 
2020-03-04 14:24:137< 0.1%
 
2020-07-11 10:49:566< 0.1%
 
2020-08-07 12:57:096< 0.1%
 
2020-06-14 11:16:326< 0.1%
 
2020-07-09 07:04:246< 0.1%
 
2020-05-07 15:01:226< 0.1%
 
2020-07-04 12:10:146< 0.1%
 
2020-06-06 12:41:496< 0.1%
 
2020-06-12 10:24:196< 0.1%
 
2020-07-11 11:59:336< 0.1%
 
Other values (2044429)2260625> 99.9%
 
2020-10-17T18:46:59.725451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1848483 ?
Unique (%)81.8%
2020-10-17T18:46:59.862086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

order_completed_at
Categorical

HIGH CARDINALITY
UNIFORM

Distinct2025311
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
2020-07-25 07:43:28
 
9
2020-07-26 12:32:27
 
7
2020-07-19 08:32:37
 
7
2020-07-25 09:05:36
 
7
2020-07-11 12:17:00
 
6
Other values (2025306)
2260650 
ValueCountFrequency (%) 
2020-07-25 07:43:289< 0.1%
 
2020-07-26 12:32:277< 0.1%
 
2020-07-19 08:32:377< 0.1%
 
2020-07-25 09:05:367< 0.1%
 
2020-07-11 12:17:006< 0.1%
 
2020-07-29 07:46:466< 0.1%
 
2020-07-24 07:47:586< 0.1%
 
2020-07-14 09:56:366< 0.1%
 
2020-07-12 11:41:036< 0.1%
 
2020-06-28 08:54:076< 0.1%
 
Other values (2025301)2260620> 99.9%
 
2020-10-17T18:47:10.702121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1813436 ?
Unique (%)80.2%
2020-10-17T18:47:10.895604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

shipment_starts_at
Categorical

HIGH CARDINALITY

Distinct7221
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
2020-05-29 12:00:00
 
1706
2020-05-29 13:00:00
 
1704
2020-05-29 11:00:00
 
1700
2020-05-30 07:00:00
 
1699
2020-08-14 12:00:00
 
1698
Other values (7216)
2252179 
ValueCountFrequency (%) 
2020-05-29 12:00:0017060.1%
 
2020-05-29 13:00:0017040.1%
 
2020-05-29 11:00:0017000.1%
 
2020-05-30 07:00:0016990.1%
 
2020-08-14 12:00:0016980.1%
 
2020-05-31 13:00:0016760.1%
 
2020-05-29 07:00:0016720.1%
 
2020-05-29 08:00:0016540.1%
 
2020-05-31 07:00:0016410.1%
 
2020-05-29 10:00:0016320.1%
 
Other values (7211)224390499.3%
 
2020-10-17T18:47:11.129976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique672 ?
Unique (%)< 0.1%
2020-10-17T18:47:11.313486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length19
Min length19

retailer
Categorical

Distinct46
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
METRO
1305570 
Лента
540033 
Ашан
363180 
МЕГАМАРТ
 
13945
Азбука Вкуса
 
6433
Other values (41)
 
31525
ValueCountFrequency (%) 
METRO130557057.8%
 
Лента54003323.9%
 
Ашан36318016.1%
 
МЕГАМАРТ139450.6%
 
Азбука Вкуса64330.3%
 
ВкусВилл62420.3%
 
ВИКТОРИЯ38870.2%
 
Командор32220.1%
 
BILLA31530.1%
 
Бахетле25250.1%
 
Other values (36)124960.6%
 
2020-10-17T18:47:11.519934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:11.731369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length21
Median length5
Mean length4.913177681
Min length3

s.order_state
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
complete
2079616 
canceled
 
177848
resumed
 
3194
cart
 
28
ValueCountFrequency (%) 
complete207961692.0%
 
canceled1778487.9%
 
resumed31940.1%
 
cart28< 0.1%
 
2020-10-17T18:47:11.906900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:12.043777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:47:12.270203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.998537612
Min length4

shipment_state
Categorical

Distinct7
Distinct (%)< 0.1%
Missing8056
Missing (%)0.4%
Memory size17.2 MiB
shipped
2075866 
canceled
 
175076
ready
 
489
collecting
 
486
ready_to_ship
 
412
Other values (2)
 
301
ValueCountFrequency (%) 
shipped207586691.8%
 
canceled1750767.7%
 
ready489< 0.1%
 
collecting486< 0.1%
 
ready_to_ship412< 0.1%
 
shipping273< 0.1%
 
pending28< 0.1%
 
(Missing)80560.4%
 
2020-10-17T18:47:12.392841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:12.473659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:47:12.598292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length7
Mean length7.064616227
Min length3

s.city_name
Categorical

HIGH CARDINALITY

Distinct97
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
Москва
539405 
Санкт-Петербург
147346 
Краснодар
124309 
Новосибирск
124021 
Екатеринбург
 
115493
Other values (92)
1210112 
ValueCountFrequency (%) 
Москва53940523.9%
 
Санкт-Петербург1473466.5%
 
Краснодар1243095.5%
 
Новосибирск1240215.5%
 
Екатеринбург1154935.1%
 
Ростов-на-Дону1127565.0%
 
Московская Область1063584.7%
 
Самара915594.1%
 
Тюмень670293.0%
 
Красноярск625792.8%
 
Other values (87)76983134.1%
 
2020-10-17T18:47:12.757868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:12.907501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length24
Median length7
Mean length8.925242603
Min length3

s.store_id
Real number (ℝ≥0)

Distinct599
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.9528276
Minimum1
Maximum726
Zeros0
Zeros (%)0.0%
Memory size17.2 MiB
2020-10-17T18:47:13.046129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q186
median129
Q3188
95-th percentile327
Maximum726
Range725
Interquartile range (IQR)102

Descriptive statistics

Standard deviation96.92464657
Coefficient of variation (CV)0.6780183939
Kurtosis2.785670167
Mean142.9528276
Median Absolute Deviation (MAD)48
Skewness1.095197914
Sum323171456
Variance9394.387113
MonotocityNot monotonic
2020-10-17T18:47:13.200683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
14467302.1%
 
2415991.8%
 
1384001.7%
 
8383741.7%
 
105369541.6%
 
10367891.6%
 
83320041.4%
 
65316981.4%
 
110311891.4%
 
11300881.3%
 
Other values (589)189686183.9%
 
ValueCountFrequency (%) 
1384001.7%
 
2415991.8%
 
3252501.1%
 
4281< 0.1%
 
8383741.7%
 
ValueCountFrequency (%) 
72642< 0.1%
 
72480< 0.1%
 
7231< 0.1%
 
72111< 0.1%
 
72017< 0.1%
 

total_cost
Real number (ℝ≥0)

ZEROS

Distinct1074
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.5803856
Minimum0
Maximum12509
Zeros494018
Zeros (%)21.9%
Memory size17.2 MiB
2020-10-17T18:47:13.367271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median158
Q3158
95-th percentile199
Maximum12509
Range12509
Interquartile range (IQR)154

Descriptive statistics

Standard deviation83.20589112
Coefficient of variation (CV)0.7261791857
Kurtosis287.526762
Mean114.5803856
Median Absolute Deviation (MAD)58
Skewness3.472797069
Sum259030273.6
Variance6923.220316
MonotocityNot monotonic
2020-10-17T18:47:13.524850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
15878378734.7%
 
049401821.9%
 
9834158615.1%
 
1991938518.6%
 
991189745.3%
 
1710123.1%
 
17888290.4%
 
16365960.3%
 
16261070.3%
 
19860080.3%
 
Other values (1064)22991810.2%
 
ValueCountFrequency (%) 
049401821.9%
 
1710123.1%
 
2.51< 0.1%
 
4718< 0.1%
 
5664< 0.1%
 
ValueCountFrequency (%) 
125091< 0.1%
 
60731< 0.1%
 
54831< 0.1%
 
50181< 0.1%
 
49681< 0.1%
 

rate
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9771733
Minimum0
Maximum5
Zeros856368
Zeros (%)37.9%
Memory size17.2 MiB
2020-10-17T18:47:13.645527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q35
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.381824985
Coefficient of variation (CV)0.8000290022
Kurtosis-1.786121872
Mean2.9771733
Median Absolute Deviation (MAD)0
Skewness-0.407830903
Sum6730454
Variance5.673090258
MonotocityNot monotonic
2020-10-17T18:47:13.748253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
5123117654.5%
 
085636837.9%
 
41048674.6%
 
3383791.7%
 
1198230.9%
 
2100730.4%
 
ValueCountFrequency (%) 
085636837.9%
 
1198230.9%
 
2100730.4%
 
3383791.7%
 
41048674.6%
 
ValueCountFrequency (%) 
5123117654.5%
 
41048674.6%
 
3383791.7%
 
2100730.4%
 
1198230.9%
 

dw_kind
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size17.2 MiB
courier
2252487 
pickup
 
6258
express_delivery
 
1941
ValueCountFrequency (%) 
courier225248799.6%
 
pickup62580.3%
 
express_delivery19410.1%
 
2020-10-17T18:47:13.875911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:13.954667image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:47:14.045426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length7
Mean length7.004959114
Min length6

promo_total
Real number (ℝ)

SKEWED
ZEROS

Distinct35671
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-80.6346039
Minimum-25000
Maximum0
Zeros1630341
Zeros (%)72.1%
Memory size17.2 MiB
2020-10-17T18:47:14.179101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-25000
5-th percentile-300
Q1-150
median0
Q30
95-th percentile0
Maximum0
Range25000
Interquartile range (IQR)150

Descriptive statistics

Standard deviation219.3843216
Coefficient of variation (CV)-2.720721762
Kurtosis1393.345312
Mean-80.6346039
Median Absolute Deviation (MAD)0
Skewness-21.09994488
Sum-182289520.1
Variance48129.48058
MonotocityNot monotonic
2020-10-17T18:47:14.338675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0163034172.1%
 
-2501864058.2%
 
-2001425376.3%
 
-300791503.5%
 
-199346761.5%
 
-150291381.3%
 
-500248941.1%
 
-100219301.0%
 
-35094460.4%
 
-40064690.3%
 
Other values (35661)957004.2%
 
ValueCountFrequency (%) 
-250006< 0.1%
 
-249982< 0.1%
 
-24822.800781< 0.1%
 
-24647.869141< 0.1%
 
-24442.449221< 0.1%
 
ValueCountFrequency (%) 
0163034172.1%
 
-0.57999998331< 0.1%
 
-0.751< 0.1%
 
-0.97000002861< 0.1%
 
-0.98000001911< 0.1%
 

total_weight
Real number (ℝ≥0)

Distinct79893
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16369.29792
Minimum0
Maximum2502000
Zeros1873
Zeros (%)0.1%
Memory size17.2 MiB
2020-10-17T18:47:14.518197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2984
Q17559
median12760
Q320810
95-th percentile40966
Maximum2502000
Range2502000
Interquartile range (IQR)13251

Descriptive statistics

Standard deviation14761.64606
Coefficient of variation (CV)0.9017885879
Kurtosis556.4665488
Mean16369.29792
Median Absolute Deviation (MAD)6091
Skewness8.470433175
Sum3.700584264e+10
Variance217906194.4
MonotocityNot monotonic
2020-10-17T18:47:14.673779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
400033220.1%
 
3000030520.1%
 
300028350.1%
 
200025770.1%
 
600023940.1%
 
500022860.1%
 
700021840.1%
 
280018880.1%
 
018730.1%
 
20017310.1%
 
Other values (79883)223654498.9%
 
ValueCountFrequency (%) 
018730.1%
 
120< 0.1%
 
248< 0.1%
 
366< 0.1%
 
46< 0.1%
 
ValueCountFrequency (%) 
25020001< 0.1%
 
11850421< 0.1%
 
11490501< 0.1%
 
11367951< 0.1%
 
11070001< 0.1%
 

platform
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing44
Missing (%)< 0.1%
Memory size17.2 MiB
app
1763573 
web
497069 
ValueCountFrequency (%) 
app176357378.0%
 
web49706922.0%
 
(Missing)44< 0.1%
 
2020-10-17T18:47:14.930095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:15.007889image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:47:15.085304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

os
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing44
Missing (%)< 0.1%
Memory size17.2 MiB
ios
929440 
android
873241 
windows
358208 
mac
 
62767
other
 
28018
ValueCountFrequency (%) 
ios92944041.1%
 
android87324138.6%
 
windows35820815.8%
 
mac627672.8%
 
other280181.2%
 
linux89680.4%
 
(Missing)44< 0.1%
 
2020-10-17T18:47:15.196034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-17T18:47:15.275821image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:47:15.398527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length7
Median length7
Mean length5.211615412
Min length3

shipped_at
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1864234
Distinct (%)89.8%
Missing185016
Missing (%)8.2%
Memory size17.2 MiB
2020-06-09 08:16:39
 
6
2020-05-29 09:31:02
 
6
2020-08-16 12:17:41
 
6
2020-05-23 10:10:59
 
6
2020-05-22 11:34:39
 
5
Other values (1864229)
2075641 
ValueCountFrequency (%) 
2020-06-09 08:16:396< 0.1%
 
2020-05-29 09:31:026< 0.1%
 
2020-08-16 12:17:416< 0.1%
 
2020-05-23 10:10:596< 0.1%
 
2020-05-22 11:34:395< 0.1%
 
2020-05-25 07:56:425< 0.1%
 
2020-05-22 16:18:155< 0.1%
 
2020-05-30 07:45:295< 0.1%
 
2020-07-05 09:57:375< 0.1%
 
2020-08-03 11:09:105< 0.1%
 
Other values (1864224)207561691.8%
 
(Missing)1850168.2%
 
2020-10-17T18:47:25.389795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1670737 ?
Unique (%)80.5%
2020-10-17T18:47:25.547374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length17.69054968
Min length3

Interactions

2020-10-17T18:44:02.086509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:04.089906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:05.296683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:06.397739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:07.508770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:08.715546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:09.816603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:10.821917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:11.819252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:12.994329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:14.181169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:15.583410image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:16.804147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:17.890246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:19.085053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:20.628465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:21.745065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:22.934404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:24.270869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:25.356931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:26.411147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:27.548075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:28.656148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:29.729246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:31.248187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:32.573647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:33.614863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:34.698967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:36.291155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:38.031505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:39.111617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:40.290469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:41.412469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:42.710003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:43.919771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:45.343967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:46.837972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:48.149477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:49.273463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:50.414448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:51.485551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:52.617559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:53.821311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:55.012129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:56.212921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:57.347887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:58.409083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:44:59.433313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:00.467566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:01.559632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:02.975852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:04.344192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:05.664662image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:06.795640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:07.924623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:09.090508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:10.186580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:11.257748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:12.439560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:13.593474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:14.696528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:15.789608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:16.852798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:17.945848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:19.384003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:20.810189image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:21.905263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:22.927532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:24.033576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:25.097734image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:26.225719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:27.321790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:28.501640image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:29.540860image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:30.642915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:31.797829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:32.964710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:34.182458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:35.580721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:36.953054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:38.167807image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:39.837348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:41.431087image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:42.566054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:43.711992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:44.814049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:45.852276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:46.890498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:48.148139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:49.280114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:50.613549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:52.278101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:53.468920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:54.514129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:55.623163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:56.792039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:57.801342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:58.813636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:45:59.873804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:46:00.936964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-17T18:47:25.677028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-17T18:47:26.030085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-17T18:47:26.296374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-17T18:47:26.591584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-17T18:47:26.868845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-17T18:46:11.866250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:46:17.003015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:46:28.597632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-17T18:46:30.981297image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexuser_idship_address_idshipment_idorder_idorder_created_atorder_completed_atshipment_starts_atretailers.order_stateshipment_states.city_names.store_idtotal_costratedw_kindpromo_totaltotal_weightplatformosshipped_at
001101917190632230721566872017-08-03 21:25:232020-02-18 14:07:002020-02-20 07:00:00METROcompleteshippedМосква21168.00courier0.030170webwindows2020-02-20 08:08:54
116227827883238702330219532018-03-02 17:22:042020-01-03 13:09:262020-01-03 17:00:00METROcompleteshippedМосква1098.00courier-150.011305webwindows2020-01-03 18:10:40
2290512646840738894330181982018-02-28 11:32:472020-02-12 12:39:282020-02-13 11:00:00METROcompleteshippedМосква2198.05courier0.013589appios2020-02-13 12:33:53
33214126196242104830302272018-03-07 20:37:272020-01-25 11:58:562020-01-25 18:00:00METROcompleteshippedМосква8158.00courier0.09726webmac2020-01-25 19:55:32
444211037829744265929239962017-12-24 11:19:042020-01-07 14:30:442020-01-07 19:00:00METROcompleteshippedМосква2163.00courier0.030323webwindows2020-01-07 19:51:37
5555080548642750203832348412018-06-21 13:14:202020-01-28 05:55:322020-01-28 09:00:00METROcompleteshippedМосква898.00courier0.010585appios2020-01-28 10:50:17
665680934592250618132287472018-06-18 07:50:252020-01-27 16:10:332020-01-28 07:00:00METROcompleteshippedМосква3258.00courier0.049598appios2020-01-28 07:43:45
777102146657951034032178942018-06-13 09:48:402020-01-26 15:55:562020-01-26 19:00:00METROcompleteshippedМосква8158.00courier0.023685appandroid2020-01-26 19:57:48
886662234642252117630846292018-04-11 13:31:292020-02-16 08:14:072020-02-16 15:00:00METROcompleteshippedМосква10158.00courier0.021000appios2020-02-16 16:06:06
9910258359573757182633745862018-08-29 10:35:502020-01-03 14:46:112020-01-03 18:00:00METROcompleteshippedМосква2158.00courier-199.07960webwindows2020-01-03 19:16:50

Last rows

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22606768727481482744123016349916468156574052020-08-27 08:33:502020-08-31 23:50:142020-09-01 10:30:00ВкусВиллcompleteNaNМосква5330.00express_delivery0.01965appiosNaN
22606778727492845749121687529916493155113072020-08-24 09:36:502020-08-31 23:45:522020-09-01 06:00:00METROcompleteNaNКемерово182158.00courier0.012266appandroidNaN
2260678872750275725125405589916504159081462020-08-31 23:38:122020-08-31 23:43:452020-09-01 09:00:00METROcanceledNaNКазань62158.00courier-300.02450appiosNaN
22606798727511813646118589029916509151674172020-08-17 21:46:332020-08-31 23:54:182020-09-03 10:00:00METROcompleteNaNМосква10116.00courier0.052320appandroidNaN
2260680872752874653118934299916515152064482020-08-18 14:14:372020-08-31 23:56:442020-09-01 05:00:00METROcompleteNaNКрасноярск119158.00courier0.01915appiosNaN
2260681872753274733121027149916517154388662020-08-22 20:34:052020-08-31 23:56:472020-09-01 12:00:00ЛентаcompleteNaNСанкт-Петербург200199.00courier-250.03990webwindowsNaN
2260682872754275725125405889916519159081832020-08-31 23:43:472020-08-31 23:45:412020-09-01 09:00:00METROcanceledNaNКазань62158.00courier-300.012390appiosNaN
2260683872755275725125405919916532159081892020-08-31 23:45:442020-08-31 23:49:032020-09-01 09:00:00METROcanceledNaNКазань62158.00courier-300.012110appiosNaN
2260684872756275725125406029916540159082032020-08-31 23:49:062020-08-31 23:50:572020-09-01 09:00:00METROcanceledNaNКазань62158.00courier-300.012390appiosNaN
22606858727572919887125199849916560158878512020-08-31 13:30:492020-08-31 23:58:222020-09-01 08:00:00METROcompleteNaNСанкт-Петербург830.00courier-300.0264webwindowsNaN